Assessing and Clustering Countries Based on COVID-19 and Related Indicators: Clustering and MULTIMOORA Approaches

Year 2024, Volume: 22 Issue: 53, 876 - 896, 22.07.2024

Pakize Yıgıt

https://doi.org/10.35408/comuybd.1373504

Abstract

The COVID-19 pandemic has been one of humanity's most difficult times. The pandemic spread and impact were not at the same level for all countries. Investigation of the variation of the countries is crucial for policymakers. Therefore, the study proposed to cluster countries according to the number of COVID-19 cases, deaths, vaccinations and related socioeconomic, disease, and health risk factors and rank them by using MULTIMOORA (MOORA plus the full multiplicative form) in an integrated way. The data set consists of 148 countries and 13 indicators. K-Means algorithm was used to cluster countries. Optimal cluster was found as six according to Silhouette Index. The cluster consisted of mostly developed countries ranked as best perform cluster. It had the highest number of COVID-19 vaccinations, GDP per capita, share health expenditure in GDP, life expectancy, elderly population portion, and environmental performance index values, and the least mortality of chronic diseases. Moreover, Norway, Iceland, and Denmark were the best-performing countries in this cluster. In addition to this, Turkey was located in the second-ranked cluster. It was also determined that COVID-19 indicators (cases, deaths, and vaccinations) were related to GDP per capita, environmental index, and life expectancy. As a result, policymakers can develop pandemic policies for country groups separately, and assistance can be provided in this regard according to the priority order of the countries.

Keywords

COVID-19, clustering analysis, K-means, MULTIMOORA

Ülkelerin COVID-19 ve İlişkili Faktörlere göre Kümelenmesi ve Değerlendirilmesi: Kümeleme ve MULTIMOORA Analizleri

Abstract

COVID-19 pandemi dönemi insanlığın yaşadığı en zor dönemlerden bir tanesidir. Pandeminin yayılımı ve etkisi bütün ülkeler için aynı deredecede olmamıştır. Ülkeler arasındaki bu farklılıkların incelenmesi politika yapıcılar için önem arzetmektedir. Bu çalışmanın amacı, ülkelerin pandemi ve ilişkili sosyoekonomik, hastalık ve sağlık risk faktörlerini kümelememek ve bir çok kriterli karar verme yöntemi olan MULTIMOORA (MOORA plus the full multiplicative form) yöntemi ile sıralanmaktır. Çalışmanın verileri, halka açık kaynaklardan elde edilmiş, 148 ülke için ve 13 değişkenden oluşmaktadır. Kümeleme analizi için K-Means algoritması kullanılmıştır. Optimal küme sayısı Silhoute Index kullanılarak altı olarak belirlenmiştir. Çoğunlukla gelişmiş ülkelerden oluşan küme birinci sırada yer almıştır. Bu kümedeki ülkeler en yüksek COVID-19 aşılama oranı, kişi başına düşen GSMH, sağlık harcamasının GSMH oranı, doğumda beklenen yaşam süresi, ve çevre performans indeksine, en düşük kronik hastalıklardan ölüm oranın sahiptir. Bunun yanında, Norveç, İzlanda ve Danimarka bu kümedeki en iyi performansa sahip ülkeler olarak bulunmuştur. Türkiye ise ikinci en iyi performansa sahip kümede yer almaktadır. COVID-19 değişkenleri (vaka sayısı, ölüm sayısı, aşılama sayısı) kişi başına düşen GSMH, çevresel performans indeksi ve doğumda beklenen yaşam süresi ile ilişkili bulunmuştur. Sonuç olarak, Politika yapıcılar ülke gruplarına yönelik ayrı ayrı COVİD-19 politikaları geliştirebilir ve ülkelerin öncelik sırasına göre pandemi konusunda yardımlar sağlanabilir.

Keywords

COVID-19, kümeleme analizi, K-means, MULTIMOORA

References

• Aydin, N., & Yurdakul, G. (2020). Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms. Applied Soft Computing Journal, 97, 106792. https://doi.org/10.1016/j.asoc.2020.106792
• Brauers, Willem K. M., Baležentis, A., & Baležentis, T. (2012). Economic Ranking of the European Union Countries by Multimoora Optimization. (April 2016), 329–335. https://doi.org/10.3846/bm.2012.043
• Brauers, Willem Karel M., & Zavadskas, E. K. (2010). Project management by multimoora as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5–24. https://doi.org/10.3846/tede.2010.01
• Brauers, Willem Karel M., & Zavadskas, E. K. (2011). Multimoora optimization used to decide on a bank loan to buy property. Technological and Economic Development of Economy, 17(1), 174–188. https://doi.org/10.3846/13928619.2011.560632
• Çağdaş, Y. (2020). Investigation of the Effects of the Coronavirus (Covid-19) Outbreak on Economy and Public Finance with Clustering Analysis. Ekonomi, Politika & Finans Araştırmaları Dergisi, 5, 137–163. https://doi.org/10.30784/epfad.811203
• Carrillo-Larco, R. M., & Castillo-Cara, M. (2020). Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach. Wellcome Open Research, 5, 56. https://doi.org/10.12688/wellcomeopenres.15819.1
• Christopher Troeger. (2023). Just How Do Deaths Due to COVID-19 Stack Up? | Think Global Health. Retrieved October 6, 2023, from https://www.thinkglobalhealth.org/article/just-how-do-deaths-due-covid-19-stack
• Dai, M., Tao, L., Chen, Z., Tian, Z., Guo, X., Allen-Gipson, D. S., … Liu, M. (2020). Influence of Cigarettes and Alcohol on the Severity and Death of COVID-19: A Multicenter Retrospective Study in Wuhan, China. Frontiers in Physiology, 11(December), 1–6. https://doi.org/10.3389/fphys.2020.588553
• Demircioğlu, M., & Eşiyok, S. (2020). Covid-19 Salgını İle Mücadelede Kümeleme Anali̇zi̇ İle Ülkeleri̇n Sınıflandırılması. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 19(Covid-19 Özel Sayısı 37), 369–389.
• Dorjee, K., Kim, H., Bonomo, E., & Dolma, R. (2020). Prevalence and predictors of death and severe disease in patients hospitalized due to COVID-19: A comprehensive systematic review and meta-analysis of 77 studies and 38,000 patients. PLoS ONE, 15(12 December), 1–27. https://doi.org/10.1371/journal.pone.0243191
• Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95–104. https://doi.org/10.1080/01969727408546059
• Gohari, K., Kazemnejad, A., Sheidaei, A., & Hajari, S. (2022). Clustering of countries according to the COVID-19 incidence and mortality rates. BMC Public Health, 22(1), 1–12. https://doi.org/10.1186/s12889-022-13086-z
• Hafezalkotob, A., Hafezalkotob, A., Liao, H., & Herrera, F. (2019). An overview of MULTIMOORA for multi-criteria decision-making: Theory, developments, applications, and challenges. Information Fusion, 51(November 2018), 145–177. https://doi.org/10.1016/j.inffus.2018.12.002
• Hair, J. F. J., Black, W. C., Babin, B. J., Anderson, R. E., Black, W. C., & Anderson, R. E. (2018). Multivariate Data Analysis. https://doi.org/10.1002/9781119409137.ch4
• Hussein, H. A., & Abdulazeez, A. M. (2021). Covid-19 Pandemic Datasets Based on Machine Learning Clustering Algorithms: A Review. Journal Of Archaeology Of Egypt/Egyptology, 18(4), 2672–2700. Retrieved from https://archives.palarch.nl/index.php/jae/article/download/6703/6488
• Kartal, E., Balaban, M. E., & Bayraktar, B. (2021). Changing Status of Global Covid-19 Outbreak in the World and in Turkey and Clustering Analysis. Istanbul Tip Fakultesi Dergisi, 84(1), 9–19. https://doi.org/10.26650/IUITFD.2020.0077
• Kocabıyık, T., Karaatlı, M., & Bolat, A. B. (2022). Clustering of OECD Countries in Accordance with Macroeconomic Variables: Comparison of Pandemic and Pre-Pamdemic Period. International Journal of Business, Economics and Management Perspectives, 6(1), 195–214. https://doi.org/http://dx.doi.org/10.29228/ijbemp.58030
• Küçükefe, B. (2020). Covid-19’un OECD Ülkeleri ve Çin’de Makroekonomik Etkisinin Kümeleme Analizi. Ekonomi, Politika & Finans Araştırmaları Dergisi, 5, 280–291. https://doi.org/10.30784/epfad.811289
• Kumru, S., Yiğit, P., & Hayran, O. (2022). Demography, inequalities and Global Health Security Index as correlates of COVID-19 morbidity and mortality. International Journal of Health Planning and Management, 37(2), 944–962. https://doi.org/10.1002/hpm.3384
• Levin, A. T., Owusu-Boaitey, N., Pugh, S., Fosdick, B. K., Zwi, A. B., Malani, A., … Meyerowitz-Katz, G. (2022). Assessing the burden of COVID-19 in developing countries: Systematic review, meta-Analysis and public policy implications. BMJ Global Health, 7(5), 1–17. https://doi.org/10.1136/bmjgh-2022-008477
• Malki, Z., Atlam, E. S., Hassanien, A. E., Dagnew, G., Elhosseini, M. A., & Gad, I. (2020). Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos, Solitons and Fractals, 138, 110137. https://doi.org/10.1016/j.chaos.2020.110137
• Moyazzem Hossain, M., Abdulla, F., & Rahman, A. (2022). Challenges and difficulties faced in low- and middle-income countries during COVID-19. Health Policy OPEN, 3(November). https://doi.org/10.1016/j.hpopen.2022.100082
• Naseer, S., Khalid, S., Parveen, S., Abbass, K., Song, H., & Achim, M. V. (2023). COVID-19 outbreak: Impact on global economy. Frontiers in Public Health, 10(5). https://doi.org/10.3389/fpubh.2022.1009393
• Our World in Data. (2023). Data, Coronavirus Pandemic (COVID-19) - Statistics and Research - Our World in. Retrieved July 8, 2023, from https://ourworldindata.org/explorers/coronavirus
• Rizvi, S. A., Umair, M., & Cheema, M. A. (2021). Clustering of countries for COVID-19 cases based on disease prevalence, health systems and environmental indicators. Chaos, Solitons and Fractals, 151, 111240. https://doi.org/10.1016/j.chaos.2021.111240
• Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(C), 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
• Sotoudeh-Anvari, A. (2022). The applications of MCDM methods in COVID-19 pandemic: A state of the art review. Applied Soft Computing, 126, 109238. https://doi.org/10.1016/j.asoc.2022.109238
• Tekin, B. (2020). COVID-19 Pandemisi Döneminde Ülkelerin COVID-19, Sağlık ve Finansal Göstergeler Bağlamında Sınıflandırılması: Hiyerarşik Kümeleme Analizi. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 5(2), 261–280. Retrieved from https://dergipark.org.tr/tr/doi/10.29106/fesa.738322
• The World Bank. (2024). World Bank Group Data. Retrieved June 19, 2023, from https://www.worldbank.org/en/home
• TÜİK. (2023). Ölüm ve Ölüm Nedeni İstatistikleri, 2022. Retrieved October 6, 2023, from https://data.tuik.gov.tr/Bulten/Index?p=Olum-ve-Olum-Nedeni-Istatistikleri-2022-49679
• Valero, M., & Valero-Gil, J. N. (2021). Determinants of the number of deaths from COVID-19: differences between low-income and high-income countries in the initial stages of the pandemic. International Journal of Social Economics, 48(9), 1229–1244. https://doi.org/10.1108/IJSE-11-2020-0752
• Weaver, A. K., Head, J. R., Gould, C. F., Carlton, E. J., & Remais, J. V. (2022). Environmental Factors Influencing COVID-19 Incidence and Severity. Annual Review of Public Health, 43, 271–291. https://doi.org/10.1146/annurev-publhealth-052120-101420
• WHO. (2023a). WHO. Retrieved June 17, 2023, from https://www.who.int/
• WHO. (2023b). WHO Coronavirus (COVID-19) Dashboard.
• Willem Karel Brauers, & Kazimieras Zavadskas, E. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35(2), 445–469.
• Wolf, M. J., Emerson, J. W., Esty, D. C., de Sherbinin, A., & Wendling, Z. A. (2022). Environmental Performance Index. https://doi.org/10.1002/9781118445112.stat03789
• Zarikas, V., Poulopoulos, S. G., Gareiou, Z., & Zervas, E. (2020). Clustering analysis of countries using the COVID-19 cases dataset. Data in Brief, 31, 105787. https://doi.org/10.1016/j.dib.2020.105787
• Zhang, F., & Baranova, A. (2022). Smoking quantitatively increases risk for COVID-19. European Respiratory Journal, 60(6), 1–4. https://doi.org/10.1183/13993003.01273-2021